Heart Failure，CongestiveweHeartClinicNCT05591443
Artificial Intelligence and Smart Wearable Technologies for Early Detection of Acute Heart Failure
An observational study of Heart Failure，Congestive, run by Centro Cardiologico Monzino.
Trial summary
Heart failure is the major pandemic of the 21st century. The number of patients and of Heart Failure-related deaths is progressively increasing. This means a devastating economic and health organization burden. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Heart Failure，Congestive
- How many people
- 120 participants
- Where
- No sites listed yet
- Study dates
- May 2023to May 2026
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with CHF will be considered for inclusion in the study based on their verified medical record, indicating that they are diagnosed with CHF and are using guideline-directed medical therapy (GDMT). Diagnostic criteria, as laid out in the latest 2021 European Society of Cardiology (ESC) guidelines for the diagnosis and management of chronic and acute heart failure, will be followed.
Who may be able to join
- Presence of symptoms and/or signs of HF
- left ventricular ejection fraction (LVEF) ≤40%. LVEF values will be obtained by determining the reduced LV systolic function, by transthoracic echocardiographic assessment as recommended by European Association of Cardiovascular Imaging (EACVI) and American Society of Echocardiography position paper.
- NYHA functional classes II-III).
Who cannot join
- NYHA functional class IV,
- Candidates for left-ventricular assist device (LVAD) or heart transplant, as per latest definition of Heart Failure Association of the ESC.
- Recent acute coronary syndrome within 1-year prior to the date of potential enrollment,
- Indirect echocardiographic evidence of significantly elevated pulmonary pressures
- Clinically relevant pulmonary hypertension
- non-adherence to optimal medical treatment for CHF
Trial locations and contacts
No sites are listed yet.
About this study
Official title: A System Based on Artificial Intelligence and Smart Wearable Technologies for Early Detection of Acute Episodes in Heart Failure Patients
Heart failure is the major pandemic of the 21st century. The number of patients and of Heart Failure-related deaths is progressively increasing. This means a devastating economic and health organization burden. In fact, chronic heart failure patients are at high risk of death, and the course of the disease is often insidious and uncertain with a progressive deterioration requiring the need for repeated and successive hospitalizations with an ominous prognosis: with each admission for acute heart failure there is a short-term improvement, a phase characterized by a degree of stability, and then a worsening phase follows until a new need for a new hospitalization. Moreover, with each subsequent hospitalization, myocardial function progressively declines, gradually worsening the patient's quality of life until the fatal event.
For these reasons, one of the major unmet needs is the identification of patients with a negative trajectory of Heart Failure. Accordingly, early identification of Heart Failure worsening is mandatory to improve patient condition and reduce Heart Failure costs, which are mainly associated with hospitalizations.
Read more
Our main goal through this project is to create clinical tool for detection of early signs of chronic heart failure (CHF) worsening that will allow timely therapeutic intervention. This timely manner intervention can lead to a much better outcome for the patient, possibly reducing the need for hospitalization or lower the number of hospitalization days.
The aim of this project is to develop clinical decision tool based on artificial intelligence (AI) algorithms to early detect the signs of exacerbation of chronic heart failure and predict the risk of its progression, by integrating high quality medical data obtained through a wearable device (L.I.F.E. Italia Srl's "wearable clinic" - a vest with accessories, which is a TRL 9 medical grade sensorized garment, already available on the market). Specifically, the focus will be on the early detection of CHF worsening in patients who have already been diagnosed with CHF.
- Conditions
- Heart Failure，Congestive
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Heart failure patients | Patients with CHF will be considered for inclusion in the study based on their verified medical record, indicating that they are diagnosed with CHF and are using guideline-directed medical therapy (GDMT). Diagnostic criteria, as laid out in the latest 2021 European Society of Cardiology (ESC) guidelines for the diagnosis and management of chronic and acute heart failure, will be followed. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Definition of an algorithm for heart failure worsening
Development by artificial intelligence of an algorithm based on all collected variables able to identify Heart Failure worsening
Measured: 6 months
Identification of respiratory predictors of heart failure worsening
Identification of which single respiratory parameters are related to Heart Failure worsening among all those collected by the L.I.F.E. device.
Measured: 6 months
Identification of ECG predictors of heart failure worsening
Identification of which single ECG parameters are related to Heart Failure worsening among all those collected by the L.I.F.E. device.
Measured: 6 months
Secondary outcomes (2)
Identification of nocturnal parameters related to heart failure worsening
Identification of which nocturnal parameters are related to Heart Failure worsening among all those collected by the L.I.F.E. device.
Measured: 6 months
Heart rate variability as marker of heart failure worsening
Evaluation of daytime/night-time/overall Heart Rate Variability as a predictor of Heart Failure worsening
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Oct 2022
- Study startMay 2023 (estimated)
- Primary completionMay 2025 (estimated)
- Study completionMay 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart Failure，CongestiveStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 120 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Centro Cardiologico Monzino
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Piergiuseppe Agostoni, Centro Cardiologico Monzino
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05591443
- Sponsor's protocol ID
- weHeartClinic
- Acronym
- weHeartClinic
- Status last verified
- Oct 2022
- First submitted
- 16 Oct 2022
- Last updated
- 24 Oct 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 24 Oct 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A System Based on Artificial Intelligence and Smart Wearable Technologies for Early Detection of Acute Episodes in Heart Failure Patients
- ClinicalTrials.gov ID
- NCT05591443
- Acronym / sponsor ID
- weHeartClinic / weHeartClinic
- Conditions
- Heart Failure，Congestive
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 120 participants (estimated)
- Lead sponsor
- Centro Cardiologico MonzinoUniversity, hospital or other
- Study start
- May 2023 (estimated)
- Primary completion
- May 2025 (estimated)
- Study completion
- May 2026 (estimated)
- First posted
- 24 Oct 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Heart failure is the major pandemic of the 21st century. The number of patients and of Heart Failure-related deaths is progressively increasing. This means a devastating economic and health organization burden. In fact, chronic heart failure patients are at high risk of death, and the course of the disease is often insidious and uncertain with a progressive deterioration requiring the need for repeated and successive hospitalizations with an ominous prognosis: with each admission for acute heart failure there is a short-term improvement, a phase characterized by a degree of stability, and then a worsening phase follows until a new need for a new hospitalization. Moreover, with each subsequent hospitalization, myocardial function progressively declines, gradually worsening the patient's quality of life until the fatal event.
For these reasons, one of the major unmet needs is the identification of patients with a negative trajectory of Heart Failure. Accordingly, early identification of Heart Failure worsening is mandatory to improve patient condition and reduce Heart Failure costs, which are mainly associated with hospitalizations.
Our main goal through this project is to create clinical tool for detection of early signs of chronic heart failure (CHF) worsening that will allow timely therapeutic intervention. This timely manner intervention can lead to a much better outcome for the patient, possibly reducing the need for hospitalization or lower the number of hospitalization days.
The aim of this project is to develop clinical decision tool based on artificial intelligence (AI) algorithms to early detect the signs of exacerbation of chronic heart failure and predict the risk of its progression, by integrating high quality medical data obtained through a wearable device (L.I.F.E. Italia Srl's "wearable clinic" - a vest with accessories, which is a TRL 9 medical grade sensorized garment, already available on the market). Specifically, the focus will be on the early detection of CHF worsening in patients who have already been diagnosed with CHF.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Heart failure patients | – | –Patients with CHF will be considered for inclusion in the study based on their verified medical record, indicating that they are diagnosed with CHF and are using guideline-directed medical therapy (GDMT). Diagnostic criteria, as laid out in the latest 2021 European Society of Cardiology (ESC) guidelines for the diagnosis and management of chronic and acute heart failure, will be followed. |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with CHF will be considered for inclusion in the study based on their verified medical record, indicating that they are diagnosed with CHF and are using guideline-directed medical therapy (GDMT). Diagnostic criteria, as laid out in the latest 2021 European Society of Cardiology (ESC) guidelines for the diagnosis and management of chronic and acute heart failure, will be followed.
Key inclusion criteria (3 listed)
- Presence of symptoms and/or signs of HF
- left ventricular ejection fraction (LVEF) ≤40%. LVEF values will be obtained by determining the reduced LV systolic function, by transthoracic echocardiographic assessment as recommended by European Association of Cardiovascular Imaging (EACVI) and American Society of Echocardiography position paper.
- NYHA functional classes II-III).
Key exclusion criteria (6 listed)
- NYHA functional class IV,
- Candidates for left-ventricular assist device (LVAD) or heart transplant, as per latest definition of Heart Failure Association of the ESC.
- Recent acute coronary syndrome within 1-year prior to the date of potential enrollment,
- Indirect echocardiographic evidence of significantly elevated pulmonary pressures
- Clinically relevant pulmonary hypertension
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Definition of an algorithm for heart failure worsening
Time frame: 6 months
Development by artificial intelligence of an algorithm based on all collected variables able to identify Heart Failure worsening
Identification of respiratory predictors of heart failure worsening
Time frame: 6 months
Identification of which single respiratory parameters are related to Heart Failure worsening among all those collected by the L.I.F.E. device.
Identification of ECG predictors of heart failure worsening
Time frame: 6 months
Identification of which single ECG parameters are related to Heart Failure worsening among all those collected by the L.I.F.E. device.
2 secondary outcomes
Identification of nocturnal parameters related to heart failure worsening
Time frame: 6 months
Heart rate variability as marker of heart failure worsening
Time frame: 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Oct 2022.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure，Congestive, run by Centro Cardiologico Monzino.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 24 Oct 2022. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Piergiuseppe Agostoni, Prof (0258002010, piergiuseppe.agostoni@ccfm.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Definition of an algorithm for heart failure worsening (time frame: 6 months). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Oct 2022.